我们同时也有很详细的introduction code供大家参考。详情请参照我们的竞赛主页:Learning and Mining with Noisy Labels。 我们也为获胜者准备了很丰厚的奖品,每一个赛道都会选出最佳参赛队伍并赢得奖金。同时我们也会选出Best innovation award。欢迎各位参与～
Deep learning with noisy labels: exploring techniques and remedies in medical image analysisSupervised training of deep learning models requires large labeled datasets. There is a growing interest in obtaining such datasets for medical image analysis applications. However, the impact of label noise has ...
为了解决这个问题,文章提出了 twin contrastive learning,它将 lable-free 的无监督表征学习和 label-noisy 的标记相结合,利用对比学习来学习判别性的表征,并基于表征构建一个GMM,需要注意的是,这个GMM不同于常规的无监督GMM,它用模型预测代替了GMM的隐变量,从而将无监督表征的学习和标记联系起来了。然后,根据学习到...
can be seamlessly integrated into the SGD optimization of the classification network. We evaluate WarPI on four benchmarks of robust learning with noisy labels and achieve the new state-of-the-art under variantnoise types. Extensive study and analysis also demonstrate the effectiveness of our ...
2* Chenyang Wang1 Deming Zhai1 Junjun Jiang1,2 Xiangyang Ji3 1Harbin Institute of Technology 2Peng Cheng Laboratory 3Tsinghua University {cszx,csxm,cswcy,zhaideming,junjunjiang}@hit.edu.cn xyji@tsinghua.edu.cn Abstract Learning with noisy labels is an important and challeng- ing t...
相比于无监督学习,learning with noisy label 更贴近深度学习在工业界的落地。典型的状态如下: 初始阶段有一定量的标注质量未知的数据。 一般会有持续的人工投入,不断提升标注质量。人工投入的形式,可能是付费众包,可能是借助用户反馈。 对...
withh(x). We compare the two strategies and we show, on different publicly available datasets, that selecting instances according to the first strategy while eliminating noisy labels according to the second strategy, greatly improves the accuracy compared to several benchmarking methods, even when a...
First, we provide a simple unbiased estimator of any loss, and obtain performance bounds for empirical risk minimization in the presence of iid data with noisy labels. If the loss function satisfies a simple symmetry condition, we show that the method leads to an efficient algorithm for ...
Learning with noisy labels means When we say "noisy labels," we mean that an adversary has intentionally messed up the labels, which would have come from a "clean" distribution otherwise. This setting can also be used to cast learning from only positive and unlabeled data....
In this paper, we propose a new task, in-context learning with noisy labels, which aims to solve real-world problems for in-context learning where labels in task demonstrations would be corrupted. Moreover, we propose a new method and baseline methods for the new task, inspired by studies ...